Dimensionality reduction by Mixed Kernel Canonical Correlation Analysis

In this paper, we propose a novel method named Mixed Kernel CCA (MKCCA) to achieve easy yet accurate implementation of dimensionality reduction. MKCCA consists of two major steps. First, the high dimensional data space is mapped into the reproducing kernel Hilbert space (RKHS) rather than the Hilber...

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Vydané v:	Pattern recognition Ročník 45; číslo 8; s. 3003 - 3016
Hlavní autori:	Zhu, Xiaofeng, Huang, Zi, Tao Shen, Heng, Cheng, Jian, Xu, Changsheng
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Kidlington Elsevier Ltd 01.08.2012 Elsevier
Predmet:	Applied sciences Artificial intelligence Canonical Correlation Analysis Computer science; control theory; systems Design engineering Dimensionality reduction Exact sciences and technology Hilbert space Image processing Information, signal and communications theory Kernels Learning Mixed kernel Model selection Noise Pattern recognition Pattern recognition. Digital image processing. Computational geometry Principal component analysis Reduction Signal processing Telecommunications and information theory Dimensionality reduction Mixed kernel Model selection Canonical Correlation Analysis Image processing Canonical correlation Implementation Kernel method Statistical method Dimension reduction Multi-view technology Canonical analysis Linear combination Correlation analysis Supervised learning Hilbert space Principal component analysis
ISSN:	0031-3203, 1873-5142
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